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Ijarcet vol-2-issue-4-1383-1388
1. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
1383
www.ijarcet.org
Abstract— The modern world is enclosed with gigantic
masses of digital visual information. Increase in the images has
urged for the development of robust and efficient object
recognition techniques. Most work reported in the literature
focuses on competent techniques for object recognition and its
applications. A single object can be easily detected in an image.
Multiple objects in an image can be detected by using different
object detectors simultaneously. The paper discusses various
techniques for object recognition and a method for multiple
object detection in an image.
Index Terms— Multi-object detection, Object recognition,
Object recognition applications.
I. INTRODUCTION
The modern world is enclosed with gigantic masses of
digital visual information. To analyze and organize these
devastating ocean of visual information image analysis
techniques are major requisite. In particular useful would be
methods that could automatically analyze the semantic
contents of images or videos. The content of the image
determines the significance in most of the potential uses. One
important aspect of image content is the objects in the image.
So there is a need for object recognition techniques.
Object recognition is an important task in image processing
and computer vision. It is concerned with determining the
identity of an object being observed in an image from a set of
known tags. Humans can recognize any object in the real
world easily without any efforts; on contrary machines by
itself cannot recognize objects. Algorithmic descriptions of
recognition task are implemented on machines; which is an
intricate task. Thus object recognition techniques need to be
developed which are less complex and efficient.
Many successful approaches that address the problem of
general object detection use a representation of the image
objects by a collection of local descriptors of the image
content. Global features provide better recognition. Color and
shape features can also be used. Various object recognition
techniques are presented in this paper. Difficulties may arise
during the process of object recognition; a range of such
difficulties are discussed in this paper. The robust and
efficient object recognition technique can be developed by
Manuscript received Feb, 2013.
Ms.Khushboo Khurana, Computer Science & Engg. department,
S.R.O.C.E.M.., Nagpur, India.
Ms. Reetu Awasthi, Computer Science department, SFS College,
Nagpur, India.
taking into account these difficulties and overcoming them.
Rest of this paper is organized as follows. Section II
elucidates various difficulties in object recognition under
varied circumstances. Section III presents various object
recognition techniques. In Section IV applications for object
recognition are discussed. In section V we have proposed a
method for multi-object detection in an image and finally, we
conclude in Section VI.
II. DIFFICULTIES IN OBJECT RECOGNITION UNDER VARIED
CIRCUMSTANCES
1. Lightning: The lightning conditions may differ during
the course of the day. Also the weather conditions
may affect the lighting in an image. In-door and
outdoor images for same object can have varying
lightning condition. Shadows in the image can affect
the image light. Whatever the lightning may be the
system must be able to recognize the object in any of
the image. Fig.1 shows same object with varying
lightning.
2. Positioning: Position in the image of the object can be
changed. If template matching is used, the system
must handle such images uniformly.
3. Rotation: The image can be in rotated form. The
system must be capable to handle such difficulty. As
shown in fig.2, the character „A‟ can appear in any of
the form. But the orientation of the letter or image
must not affect the recognition of character „A‟ or
any image of object.
4. Mirroring: The mirrored image of any object must be
recognized by the object recognition system.
Khushboo Khurana, Reetu Awasthi
Techniques for Object Recognition in Images
and Multi-Object Detection
Fig.1 Objects with different lightning.
Fig.2 Different orientation of character „A‟
3. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
1385
www.ijarcet.org
In [5] the aim is to arrive at recognition of multicolored
objects invariant to a substantial change in viewpoint,
object geometry and illumination. Assuming dichromatic
reßectance and white illumination, it is shown that
normalized color rgb, saturation S and hue H, and the
newly proposed color models c1c2c3 and l1l2l3 are all
invariant to a change in viewing direction, object geometry
and illumination. Further, it is shown that hue H and l1l2l3
are also invariant to highlights. Finally, a change in spectral
power distribution of the illumination is considered to
propose a new color constant color model m1m2m3. To
evaluate the recognition accuracy di¤erentiated for the
various color models, experiments have been carried out on
a database consisting of 500 images taken from 3-D
multicolored man-made objects. The experimental results
show that highest object recognition accuracy is achieved
by l1l2l3 and hue H followed by c1c2c3, normalized color
rgb and m1m2m3 under the constraint of white
illumination. Also, it is demonstrated that recognition
accuracy degrades substantially for all color features other
than m1m2m3 with a change in illumination color. In this
paper the authors aim to examine and evaluate a variety of
color models used for recognition of multicolored objects
according to the following criteria:
1. Robustness to a change in viewing direction
2. Robustness to a change in object geometry
3. Robustness to a change in the direction of the
illumination
4. Robustness to a change in the intensity of the
illumination
5. Robustness to a change in the spectral power
distribution (SPD) of the illumination.
The color models have High discriminative power;
robustness to object occlusion and cluttering; robustness to
noise in the images.
C. Active and Passive
Object detection in passive manner does not involve local
image samples extracted during scanning. Two main
object-detection approaches that employ passive scanning:
1. The window-sliding approach: It uses passive
scanning to check if the object is present or not at all
locations of an evenly spaced grid. This approach
extracts a local sample at each grid point and
classifies it either as an object or as a part of the
background [6].
2. The part-based approach: It uses passive scanning to
determine interest points in an image. This approach
calculates an interest value for local samples at all
points of an evenly spaced grid. At the interest
points, the approach extracts new local samples that
are evaluated as belonging to the object or the
background [7].
Some methods try to bound the region of the image in
which passive scanning is applied. It is a computationally
expensive and inefficient scanning method. In this method
at each sampling point costly feature extraction is
performed, while the probability of detecting an object or
suitable interest point can be squat.
In active scanning local samples are used to guide the
scanning process. At the current scanning position a local
image sample is extracted and mapped to a shifting vector
indicating the next scanning position. The method takes
successive samples towards the expected object location,
while skipping regions unlikely to contain the object. The
goal of active scanning is to save computational effort,
while retaining a good detection performance [8].
The active object-detection method (AOD-method) scans
the image for multiple discrete time steps in order to find an
object. In the AOD-method this process consists of three
phases:
(i) Scanning for likely object locations on a coarse scale
(ii) Refining the scanning position on a fine scale
(iii)Verifying object presence at the last scanning
position with a standard object detector.
D. Shape based
Recently, shape features have been extensively
explored to detect objects in real-world images. The shape
features are more striking as compared to local features like
SIFT because most object categories are better described by
their shape then texture, such as cows, horses and cups and
also for wiry objects like bikes, chair or ladders, local features
unavoidably contain large amount of background mess. Thus
shape features are often used as a replacement or complement
to local features.
A. Berg, et.al. [9], have proposed a new algorithm to find
correspondences between feature points for object
recognition in the framework of deformable shape matching.
The basic subroutine in deformable shape matching takes as
input an image with an unknown object (shape) and compares
it to a model by solving the correspondence problem between
the model and the object. Then it performs aligning
transformation and computes a similarity based on both the
aligning transform and the residual after applying the aligning
transformation. The Authors have considered various reasons
like Intra-category variation, Occlusion and clutter, 3D pose
changes that makes correspondence problems more difficult.
Three kinds of constraints to solve the correspondence
problem between shapes are Corresponding points on the two
shapes should have similar local descriptors, Minimizing
geometric distortion, Smoothness of the transformation from
one shape to the other.
In [10], a new shape-based object detection scheme of
extraction and clustering of edges in images using Gradient
vector Girding (GVG) method is proposed that results a
directed graph of detected edges. The algorithm used contains
a sequential pixel-level scan, and a much smaller second and
third pass on the results to determine the connectiveness. The
graph is built on cell basis and the image is overlaid with a
grid formed of equal sized cells. Multiple graph nodes are
computed for individual cells and then connected
corresponding to the connectivity in the 8-neighbourhood of
each cell. Finally, the maximum curvature of the result paths
is adjusted. The Authors have also proposed several
techniques to increase the performance of the method and
5. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
1387
www.ijarcet.org
IV. APPLICATION OF OBJECT RECOGNITION
1. Biometric recognition: Biometric technology uses
human physical or behavioral traits to recognize any
individual for security and authentication [19].
Biometrics is the identification of an individual
based on distinguished biological features such as
finger prints, hand geometry, retina and iris patterns,
DNA, etc. For biometric analysis, object recognition
techniques such as template matching can be used.
2. Surveillance: Objects can be recognized and tracked
for various video surveillance systems. Object
recognition is required so that the suspected person
or vehicle for example be tracked.
3. Industrial inspection: Parts of machinery can be
recognized using object recognition and can be
monitored for malfunctioning or damage.
4. Content-based image retrieval (CBIR): When the
retrieval is based on the image content it is referred
as CBIR. A supervised learning system, called
OntoPic, which provides an automated keyword
annotation for images and content–based image
retrieval is presented in [20].
5. Robotic: The research of autonomous robots is one of
the most important issues in recent years. The
humanoid robot soccer competition is very popular.
The robot soccer players rely on their vision systems
very heavily when they are in the unpredictable and
dynamic environments. The vision system can help
the robot to collect various environment information
as the terminal data to finish the functions of robot
localization, robot tactic, barrier avoiding, etc. It can
decrease the computing efforts, to recognize the
critical objects in the contest field by object features
which can be obtained easily by object recognition
techniques [21].
6. Medical analysis: Tumour detection in MRI images,
skin cancer detection can be some examples of
medical imaging for object recognition.
7. Optical character/digit/document recognition:
Characters in scanned documents can be recognized
by recognition techniques.
8. Human computer interaction: Human gestures can be
stored in the system, which can be used for
recognition in the real-time environment by
computer to do interaction with humans. The system
can be any application on mobile phone, interactive
games, etc.
9. Intelligent vehicle systems: Intelligent vehicle systems
are needed for traffic sign detection and recognition,
especially for vehicle detection and tracking. In [18],
such a system is developed. In detection phase, a
color-based segmentation method is used to scan the
scene in order to quickly establish regions of interest
(ROI). Sign candidates within ROIs are detected by
a set of Haar wavelet features obtained from
AdaBoost training. Then, the Speeded Up Robust
Features (SURF) is applied for the sign recognition.
SURF finds local invariant features in a candidate
sign and matches these features to the features of
template images that exist in data set. The
recognition is performed by finding out the template
image that gives the maximum number of matches.
V. METHOD FOR MULTI-OBJECT DETECTION IN AN IMAGE
A single image may consist of single or multiple objects. If
all the objects in an image need to be detected the method
shown in fig.7 can be used.
The method trains different object detectors with individual
objects, as shown in fig.7. there are N object detectors which
are trained to detect N different objects. Any of the above
mentioned object recognition techniques can be used
depending upon the application area. An image is provided as
input to the system. The same image is given as input to all
object detectors. Each detector will determine if the object is
present or not. We propose to use object detector along with
boundary detector. If the object is present, the detector will
find its boundary and tag the object name in the image.
So, after the image has passed via all the detectors all objects
will be detected along with object boundary and its tag
displayed in the output image.
Also, when the output image is displayed, we can move the
cursor over the image. The tag shown for an object inside the
complete boundary of the object remains same. Such
multi-object detection in the image can greatly improve the
performance of the content based image retrieval systems.
The performance can further be improved by letting the
object detectors run in parallel.
VI. CONCLUSION
In this paper, we have discussed various object detection
techniques. The template matching technique requires large
database of image templates for correct object recognition.
Hence it must be used only when limited objects are to be
detected. Global features and shape based method can give
Fig.7. Method for multi-object detection in an image